I’m passionate about open-source science, so I had to give Big Ups to Neal Caren who I just learned is sharing code on github. His latest offering essentially replicates the Mark Regnerus study of children whose parents had same-sex relationships. The writeup of this exercise is at Scatterplot.

My previous posts on github and sharing code are here and here. If you’re on github, follow me.

Ok, in my research methods class, we are hitting an overview of statistics in the closing weeks of the semester. As such, i would prefer to include some empirical examples to visualize the things we’re going to talk about that are fun / outside my typical wheelhouse. So, do you have any favorite (read: typical, atypical, surprising, bizarre, differentially distributed, etc.) examples of univariate distributions and/or bivariate associations that may “stick” in their memories when they see them presented visually? I have plenty of “standard” examples i could draw from, but they’re likely bored with the one’s i think of first by this point in the term. So, what are yours? It’s fine if you just have the numbers, i can convert them to visualizations, but if you have visual pointers, all the better.

I came back from that class to this article pointing to a debate on voter ID laws, and I couldn’t help but think that there has to be a meaningful way to throw this method at this question to estimate plausible bounds for the actual potential impact of these laws. And furthermore, it seems especially important because people without IDs are likely quite hard to accurately enumerate on there own (as are those who’ve engaged in voter fraud).

So, has this study already been published and i just missed it? Else, does someone have the data we’d need for that? I’m hoping it’s a solved question, as i assume its something it would be better to have known a few months ago than a few weeks from now. Anywho, just puzzling over a salient question that linked together some events from my day.

Dan Hirschman has a great review of the new book on quantitative and qualitative methodology by Goertz and Mahoney.

One of the things Goertz and Mahoney offer are two lists describing the different tendencies of quantitative and qualitative work. I’d like to briefly comment on a couple of the contrasts which are accurate descriptions of common practice in quantitative methodology, but less so of best practice. The first issue is how quants and quals think about non-linearity, the second is about their preference for within vs. across case variation.

After describing how qual researchers account for non-linearity, Dan says:

Of course, a quantitative model could accommodate these sorts of conceptual mass points, but it’s very much against the norms of the culture. Instead, we’d tend to load GDP/capita (or maybe log GDP/capita) into a regression equation, which thus implicitly assumes that all variation is meaningful, and that an extra $1000 is equally meaningful across the spectrum (or that a change of 10% is equally meaningful, in the log context).

I wouldn’t say modeling non-linearity is against the norms of the culture. In fact, a failure to do so is something quant experts consider an elementary flaw. Its interesting that it nonetheless gets through peer review so often. Even if modeling non-linearity is part of agreed upon best practices, it is interesting and important that, as Dan says, it often isn’t done.

The book also observes that quants, compared to quals, are more likely to emphasize between case variation as compared to within case variation. I think there is something to this, but one of the things that distinguishes the most rigorous quantitative research is that it often capitalizes on within case variation from panel data.

Keep in mind that I haven’t read the book, so I’m not sure the extent to which I’m responding to Dan vs. responding to Goertz and Mahoney. But regardless, you should go read Dan’s review… its quite interesting.

I love blogging about blogs, so let me point you to a new working paper entitled “Do Political Blogs Matter? Corruption in State-Controlled Companies, Blog Postings, and DDoS Attacks.” I certainly like the idea that blogs can be tools to fight corruption. But, and I say this as someone who hasn’t read the paper, I don’t know how much should we care about the result that online criticism caused very short-term changes in stock prices. Perhaps Brayden King, with his interest in activism directed towards private companies, would have an interesting comment.

The authors are economists, Ruben Enikolopov, Maria Petrova, and Konstantin Sonin. The paper is here and the abstract:

Though new media has become a popular source of information, it is less clear whether or not they have a real impact on economic activity. In authoritarian regimes, where the traditional media are not free, this potential impact might be especially important. We study consequences of blog postings of a popular Russian anti-corruption blogger and shareholder activist Alexei Navalny on the stock prices of state-controlled companies. In an event-study analysis, we find a negative effect of company-related blog postings on both daily abnormal returns and within-day 5-minute returns. To cope with identification problem, we use the incidence of distributed denial-of-services (DDoS) attacks as a variable that negatively affects blog postings, but is uncorrelated with other determinants of asset prices. There is a substantial positive effect of the DDoS attacks on abnormal returns of the companies Navalny wrote about, and this effect is increasing in amount of his attention to these companies. The effect is decreasing in attention to posts of other top bloggers, increasing in visitors’ attention to Navalny’s posts, and is consistent with more pronounced individual, in contrast to institutional, trading. Finally, there are long-term effects of certain types of posts on stock returns, trading volume, and volatility. Overall, our evidence implies that blog postings about corruption in state-controlled companies have a negative causal impact on stock performance of these companies.